Most existing or custom Similarities have configuration options which
can be configured via the index settings as shown below. The index
options can be provided when creating an index or updating index
settings.

Another TF/IDF based similarity that has built-in tf normalization and
is supposed to work better for short fields (like names). See
Okapi_BM25 for more details.
This similarity has the following options:

k1

Controls non-linear term frequency normalization
(saturation).

b

Controls to what degree document length normalizes tf values.

discount_overlaps

Determines whether overlap tokens (Tokens with
0 position increment) are ignored when computing norm. By default this
is true, meaning overlap tokens do not count when computing norms.

By default, Elasticsearch will use whatever similarity is configured as
default. However, the similarity functions queryNorm() and coord()
are not per-field. Consequently, for expert users wanting to change the
implementation used for these two methods, while not changing the
default, it is possible to configure a similarity with the name
base. This similarity will then be used for the two methods.